Biology subjects

Scott, L.

Publications and source records attributed to Scott, L..

4 recordsLinked to original sources

bioRxiv2018

Bioinformatic analysis reveals mechanisms underlying parasitoid venom evolution and function

Introduction Introduction Methods Results and Discussion Conclusions References Parasitoid wasps are a numerous and diverse group of insects that obligately infect other arthropod species. These wasps lay their eggs either on the surface or within the body cavity of their hosts, and the resulting offspring exploit the hosts resources to complete their development [1]. Many parasitoids also introduce venom gland derived proteins or polydnaviruses into the host during infection. These factors act through a variety of mechanisms to manipulate host biology in order to increase the fitness of the developing parasitoid offspring [2-4]. Many hosts mount immune responses to parasitoid infection, and accordingly, parasitoids have evolved multiple venom genes that encode immunom ...

genomics↗

bioRxiv2018

Genetic discovery and translational decision support from exome sequencing of 20,791 type 2 diabetes cases and 24,440 controls from five ancestries

Protein-coding genetic variants that strongly affect disease risk can provide important clues into disease pathogenesis. Here we report an exome sequence analysis of 20,791 type 2 diabetes (T2D) cases and 24,440 controls from five ancestries. We identify rare (minor allele frequency<0.5%) variant gene-level associations in (a) three genes at exome-wide significance, including a T2D-protective series of >30 SLC30A8 alleles, and (b) within 12 gene sets, including those corresponding to T2D drug targets (p=6.1x10-3) and candidate genes from knockout mice (p=5.2x10-3). Within our study, the strongest T2D rare variant gene-level signals explain at most 25% of the heritability of the strongest common single-variant signals, and the rare variant gene-level effect sizes we observe in established T2D drug targets will require 110K-180K sequenced cases to exceed exome-wide significance. To help prioritize genes using associations from current smaller sample sizes, we present a Bayesian framework to recalibrate association p-values as posterior probabilities of association, estimating that reaching p<0.05 (p<0.005) in our study increases the odds of causal T2D association for a nonsynonymous variant by a factor of 1.8 (5.3). To help guide target or gene prioritization efforts, our data are freely available for analysis at www.type2diabetesgenetics.org.

genetics↗

bioRxiv2017

Multi-SKAT: General framework to test multiple phenotype associations of rare variants

In genetic association analysis, a joint test of multiple distinct phenotypes can increase power to identify sets of trait-associated variants within genes or regions of interest. Existing multi-phenotype tests for rare variants make specific assumptions about the patterns of association with underlying causal variants and the violation of these assumptions can reduce power to detect association. Here we develop a general framework for testing pleiotropic effects of rare variants on multiple continuous phenotypes using multivariate kernel regression (Multi-SKAT). Multi-SKAT models effect sizes of variants on the phenotypes through a kernel matrix and performs a variance component test of association. We show that many existing tests are equivalent to specific choices of kernel matrices with the Multi-SKAT framework. To increase power of detecting association across tests with different kernel matrices, we developed a fast and accurate approximation of the significance of the minimum observed p-value across tests. To account for related individuals, our framework uses random effects for the kinship matrix. Using simulated data and amino acid and exome-array data from the METSIM study, we show that Multi-SKAT can improve power over single-phenotype SKAT-O test and existing multiple phenotype tests, while maintaining type I error rate.\n\nGrant NumbersThis research was conducted with support from the following grants from National Institute of Health: O_LIR01 HG008773 (D.D. and S.L.)\nC_LIO_LIR01 LM012535 (D.D. and S.L.)\nC_LIO_LIR01 HG000376 (L.S. and M.B.)\nC_LIO_LIU01 DK062370 (L.S. and M.B.)\nC_LI

genetics↗

bioRxiv2017

Genomewide association study identifies 30 loci associated with bipolar disorder

Bipolar disorder is a highly heritable psychiatric disorder that features episodes of mania and depression. We performed the largest genome-wide association study to date, including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 sentinel variants at loci with P<1x10-4 in an independent sample of 9,412 cases and 137,760 controls. In the combined analysis, 30 loci reached genome-wide significant evidence for association, of which 20 were novel. These significant loci contain genes encoding ion channels and neurotransmitter transporters (CACNA1C, GRIN2A, SCN2A, SLC4A1), synaptic components (RIMS1, ANK3), immune and energy metabolism components. Bipolar disorder type I (depressive and manic episodes; ~73% of our cases) is strongly genetically correlated with schizophrenia whereas bipolar disorder type II (depressive and hypomanic episodes; ~17% of our cases) is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential new biological mechanisms for bipolar disorder.

genetics↗